Systems and methods for processing fragment ion spectra to determine mechanism of fragmentation and structure of molecule

ABSTRACT

Correlated fragment ions of a molecule are grouped using mass spectrometry with ramps in collision energy (CE). A known molecule is fragmented and analyzed at a plurality of different collision energies using a mass spectrometer. A plurality of variables for a plurality of fragment ions are produced. Principal component analysis is performed on the plurality of variables. A number of principal components produced by the principal component analysis is selected. A subset principal component space is created having the number of principal components. A variable in the subset principal component space is selected. A spatial angle is defined around a vector extending from an origin to the variable. A set of one or more variables within the spatial angle of the vector is selected. The set is assigned to a group, if the set includes a minimum number of variables.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation-in-part application of U.S.application Ser. No. 12/474,418 filed on May 29, 2009, which is acontinuation-in-part application of U.S. application Ser. No.12/200,636, filed on Aug. 28, 2008, now U.S. Pat. No. 8,073,639, whichis a continuation-in-part application of U.S. application Ser. No.11/848,717, filed on Aug. 31, 2007, now U.S. Pat. No. 7,587,285 andwhich claims the benefit of U.S. Provisional Application Ser. No.61/057,719, filed on May 30, 2008; this application also claims thebenefit of U.S. Provisional Application Ser. No. 61/582,271, filed onDec. 31, 2011. All of the above mentioned applications are incorporatedby reference herein in their entireties.

INTRODUCTION

The elucidation of a chemical structure is very difficult using massspectrometry/mass spectrometry (MS/MS). The mechanism by which afragment ion is generated requires a high degree of skill and input fromprior chemical and mechanistic chemistry knowledge. However, the aim ofsuch experiments is to provide an insight into the structure of amolecule.

Historically this has been performed by undertaking lengthy experiments.These experiments use ramps in collision energy (CE). The decompositionprofiles of the different ions are then plotted to determine thepotential order by which the fragment occurs. The potential order bywhich the fragment occurs helps in fitting together the differentcomponents.

Such a method is fine for the components that comprise the majority ofthe spectra i.e. primary fragments. But in any fragment ion spectra, thenumber of fragment ions generated is large. It is these ions which cansupport a defined mechanism or propose novel mechanisms of fragmentationand hence assist in the determination of the molecular structure. It isnormal to threshold spectra prior to analysis removing these fragmentions as the total number of ions can be very large if all are taken intoaccount. This results in potentially important information being lost.

BRIEF DESCRIPTION OF THE APPENDICES AND DRAWINGS

Appendix 1 is an exemplary description a method for identifying thestructure of molecule using spectrometry/mass spectrometry (MS/MS) withramps in collision energy (CE), in accordance with various embodiments.

FIG. 1 is a block diagram that illustrates a computer system, inaccordance with various embodiments.

FIG. 1 is a block diagram that illustrates a computer system, upon whichembodiments of the present teachings may be implemented.

FIG. 2 is an exemplary flowchart showing a method for identifying agroup of correlated variables after principal component analysis of aplurality of variables from a plurality of samples using principalcomponent variable grouping (PCVG) that is consistent with the presentteachings.

FIG. 3 is an exemplary illustration that shows how a set of one or morevariables can be found within a spatial angle of a selected variable, inaccordance with the present teachings.

FIG. 4 is an exemplary schematic diagram showing a computing system forgrouping variables after PCA of a plurality of variables from aplurality of samples produced by a measurement technique that isconsistent with the present teachings.

FIG. 5 is an exemplary flowchart showing a computer-implemented methodthat can be used for processing data in n-dimensional space and that isconsistent with the present teachings.

FIG. 6 is an exemplary image of a user interface for a software tool toperform variable grouping, in accordance with the present teachings.

FIG. 7 is an exemplary scores plot of two principal components (PCs) forMS spectra data obtained after Pareto scaling and PCA, in accordancewith the present teachings.

FIG. 8 is an exemplary loadings plot of two PCs for MS spectra dataobtained after Pareto scaling and PCA, in accordance with the presentteachings.

FIG. 9 is an exemplary profile plot of a few representative variablesfrom MS spectra data obtained after Pareto scaling and PCA, inaccordance with the present teachings.

FIG. 10 is a flowchart showing a method for identifying a convolvedpeak, in accordance with the present teachings.

FIG. 11 is an exemplary plot of a convolved peak from a spectrum, inaccordance with the present teachings.

FIG. 12 is an exemplary plot of how intensity for each mass of a firstgroup varies across samples, in accordance with the present teachings.

FIG. 13 is an exemplary plot of how intensity for each mass of a secondgroup varies across samples, in accordance with the present teachings.

FIG. 14 is an exemplary plot of how intensity for each mass of a thirdgroup varies across samples, in accordance with the present teachings.

FIG. 15 is a schematic diagram showing a system for identifying groupsof correlated representations of variables from a large amount ofspectrometry data, in accordance with the present teachings.

FIG. 16 is a flowchart showing a method for identifying groups ofcorrelated representations of variables from a large amount ofspectrometry data, in accordance with the present teachings.

FIG. 17 is a schematic diagram of a system of distinct software modulesthat performs a method for identifying groups of correlatedrepresentations of variables from a large amount of spectrometry data,in accordance with the present teachings.

FIG. 18 is a schematic diagram showing a system for grouping fragmentions of a molecule using tandem mass spectrometry with ramps incollision energy, in accordance with various embodiments.

FIG. 19 is an exemplary flowchart showing a method for grouping fragmentions of a molecule using mass spectrometry with ramps in collisionenergy, in accordance with various embodiments.

FIG. 20 is a schematic diagram of a system that includes one or moredistinct software modules that performs a method for grouping fragmentions of a molecule using mass spectrometry with ramps in collisionenergy, in accordance with various embodiments.

Before one or more embodiments of the present teachings are described indetail, one skilled in the art will appreciate that the presentteachings are not limited in their application to the details ofconstruction, the arrangements of components, and the arrangement ofsteps set forth in the following detailed description or illustrated inthe drawings. Also, it is to be understood that the phraseology andterminology used herein is for the purpose of description and should notbe regarded as limiting.

DESCRIPTION OF VARIOUS EMBODIMENTS Computer-Implemented System

FIG. 1 is a block diagram that illustrates a computer system 100, uponwhich embodiments of the present teachings may be implemented. Computersystem 100 includes a bus 102 or other communication mechanism forcommunicating information, and a processor 104 coupled with bus 102 forprocessing information. Computer system 100 also includes a memory 106,which can be a random access memory (RAM) or other dynamic storagedevice, coupled to bus 102 for storing instructions to be executed byprocessor 104. Memory 106 also may be used for storing temporaryvariables or other intermediate information during execution ofinstructions to be executed by processor 104. Computer system 100further includes a read only memory (ROM) 108 or other static storagedevice coupled to bus 102 for storing static information andinstructions for processor 104. A storage device 110, such as a magneticdisk or optical disk, is provided and coupled to bus 102 for storinginformation and instructions.

Computer system 100 may be coupled via bus 102 to a display 112, such asa cathode ray tube (CRT) or liquid crystal display (LCD), for displayinginformation to a computer user. An input device 114, includingalphanumeric and other keys, is coupled to bus 102 for communicatinginformation and command selections to processor 104. Another type ofuser input device is cursor control 116, such as a mouse, a trackball orcursor direction keys for communicating direction information andcommand selections to processor 104 and for controlling cursor movementon display 112. This input device typically has two degrees of freedomin two axes, a first axis (i.e., x) and a second axis (i.e., y), thatallows the device to specify positions in a plane.

A computer system 100 can perform the present teachings. Consistent withcertain implementations of the present teachings, results are providedby computer system 100 in response to processor 104 executing one ormore sequences of one or more instructions contained in memory 106. Suchinstructions may be read into memory 106 from another computer-readablemedium, such as storage device 110. Execution of the sequences ofinstructions contained in memory 106 causes processor 104 to perform theprocess described herein. Alternatively hard-wired circuitry may be usedin place of or in combination with software instructions to implementthe present teachings. Thus implementations of the present teachings arenot limited to any specific combination of hardware circuitry andsoftware.

The term “computer-readable medium” as used herein refers to any mediathat participates in providing instructions to processor 104 forexecution. Such a medium may take many forms, including but not limitedto, non-volatile media, volatile media, and transmission media.Non-volatile media includes, for example, optical or magnetic disks,such as storage device 110. Volatile media includes dynamic memory, suchas memory 106. Transmission media includes coaxial cables, copper wire,and fiber optics, including the wires that comprise bus 102.

Common forms of computer-readable media include, for example, a floppydisk, a flexible disk, hard disk, magnetic tape, or any other magneticmedium, a CD-ROM, digital video disc (DVD), a Blu-ray Disc, any otheroptical medium, a thumb drive, a memory card, a RAM, PROM, and EPROM, aFLASH-EPROM, any other memory chip or cartridge, or any other tangiblemedium from which a computer can read.

Various forms of computer readable media may be involved in carrying oneor more sequences of one or more instructions to processor 104 forexecution. For example, the instructions may initially be carried on themagnetic disk of a remote computer. The remote computer can load theinstructions into its dynamic memory and send the instructions over anetwork. The remote computer can receive data over the network and placethe data on bus 102. Bus 102 carries the data to memory 106, from whichprocessor 104 retrieves and executes the instructions. The instructionsreceived by memory 106 may optionally be stored on storage device 110either before or after execution by processor 104.

In accordance with various embodiments, instructions configured to beexecuted by a processor to perform a method are stored on acomputer-readable medium. The computer-readable medium can be a devicethat stores digital information. For example, a computer-readable mediumincludes a compact disc read-only memory (CD-ROM) as is known in the artfor storing software. The computer-readable medium is accessed by aprocessor suitable for executing instructions configured to be executed.

The following descriptions of various implementations of the presentteachings have been presented for purposes of illustration anddescription. It is not exhaustive and does not limit the presentteachings to the precise form disclosed. Modifications and variationsare possible in light of the above teachings or may be acquired frompracticing of the present teachings. Additionally, the describedimplementation includes software but the present teachings may beimplemented as a combination of hardware and software or in hardwarealone. The present teachings may be implemented with bothobject-oriented and non-object-oriented programming systems.

PCA

Principal component analysis (PCA) is a multivariate analysis (MVA) toolthat is widely used to help visualize and classify data. PCA is astatistical technique that may be used to reduce the dimensionality of amulti-dimensional dataset while retaining the characteristics of thedataset that contribute most to its variance. For this reason PCA isoften used to pre-process data for techniques that do not handle highdimensionality data well such as linear discriminant analysis (LDA).

PCA can reduce the dimensionality of a large number of interrelatedvariables by using an eigenvector transformation of an original set ofvariables into a substantially smaller set of principal component (PC)variables that represents most of the information in the original set.The new set of variables is ordered such that the first few retain mostof the variation present in all of the original variables. Moreparticularly, each PC is a linear combination of all the originalmeasurement variables. The first is a vector in the direction of thegreatest variance of the observed variables. The succeeding PCs arechosen to represent the greatest variation of the measurement data andto be orthogonal to the previously calculated PC. Therefore, the PCs arearranged in descending order of importance. The number of PCs (n)extracted by PCA cannot exceed the smaller of the number of samples orvariables. However, many of the variables may correspond to noise in thedata set and contain no useful information.

PCA requires that data be presented in the form of a matrix (hereafterreferred to as “the Input Matrix”) where, for example, rows representsamples, columns represent variables, and an element or cell of theInput Matrix indicates the amount of that variable in a particularsample. Alternatively, the Input Matrix can include rows that representvariables, columns that represent samples, and elements that representthe amount of that variable in a particular sample. In the latter case,the processing described as applied to a loadings plot is insteadapplied to a scores plot. An Input Matrix can be decomposed into aseries of score and loading vectors. The loading vectors indicate thecontribution that each variable makes to a particular PC. The scorevectors are a measure of the amount of each component in a particularsample.

Scores and loadings plots can be displayed where the axes represent twoor more PCs, the samples are positioned according to their scores, andthe variables are positioned according to the loadings. The scoresreflect the amount of each PC present in the sample while the loadingsindicate the importance of each variable to the PC.

Although PCA is an unsupervised technique requiring no knowledge of anysample groups, this information is frequently available and helps tointerpret the scores plot. Knowledge about sample groups can, forexample, help determine if the samples separate in an expected way ornot. In contrast to the scores plot, the loadings plot can be verydifficult to interpret, especially when there are many variables andnone are dominant, or the data has been autoscaled to remove the effectof intensity.

Although it is common to remove correlated variables prior to PCA, theiridentification can help further interpretation. For example, in massspectral data, correlated peaks may be unpredictable fragments or mayhave known origins including, but not limited to, isotopes, adducts, anddifferent charge states. Recognizing unpredictable fragments can helpidentify the compound that generated the spectrum. Consequently, it canbe beneficial to retain all variables extracted from the raw data,rather than removing the correlated variables before performing PCA,since this allows the loadings plots to be interpreted to findcorrelated features. Essentially, PCA is using the variables to separateand group the samples, but it is also using the samples to separate andcluster the variables. Once the correlated variables have beenidentified, they can be simplified in a number of ways including, forexample, replacing a set of correlated variables with some grouprepresentation including, but not limited to, the most intense variableof the correlated variables, a new variable with the mean intensity ofthe correlated variables, or the sum of the correlated variables.

Methods of Data Processing

Principal Component Variable Grouping

In various embodiments, groups of correlated variables are identifiedusing principal component analysis (PCA) followed by variable grouping.PCA followed by variable grouping can be called principal componentvariable grouping (PCVG).

FIG. 2 is an exemplary flowchart showing a method 200 for identifying agroup of correlated variables after PCA of a plurality of variables froma plurality of samples using PCVG that is consistent with the presentteachings.

In step 210 of method 200, a number of PCs produced by the PCA isselected. The number of PCs selected is, for example, less than thetotal number of PCs produced by the PCA. In various embodiments, thenumber of PCs selected is the smallest number that represents aspecified percentage of the total variance.

In step 220, a subset PC space having the number of PCs selected iscreated.

In step 230, a variable is selected in the subset PC space. The variableselected is, for example, the variable that is furthest from the origin.

In step 240, a spatial angle is defined around a vector extending fromthe origin of the subset PC space to the selected variable.

In step 250, a set of one or more variables in the subset PC space isselected within the spatial angle of the vector. In various embodiments,if one or more variables within the set have a significance value lessthan a threshold value, then the one or more variables are not selectedfor the first set. The significance value is a minimum distanceparameter, for example. The minimum distance parameter is a minimumdistance from the origin, for example.

In step 260, the set is assigned to a group, if the set includes aminimum number of variables. The group identifies correlated variables,for example. The minimum number of variables is the number of correlatedvariables a group is expected to include, for example. The minimumnumber of variables can be, for example, one or a number greater thanone.

In various embodiments, method 200 can also include calculating a secondvector from the group, selecting a second set of one or more variableswithin the spatial angle of the second vector, and replacing thevariables of the group with the variables of the second set, if thesecond set includes a minimum number of variables. The spatial angle ofthe second vector can be the same spatial angle defined in step 240, orthe spatial angle of the second vector can be a spatial and that isdifferent from the spatial angle defined in step 240. The second vectorcan be any linear or nonlinear combination of the variables in thegroup. For example, the second vector can be, but is not limited to, thearithmetic mean, a weighted mean, the median, or the geometric mean. Invarious embodiments, if one or more variables within the second set havea significance value less than a threshold value, then the one or morevariables are not selected for the second set. The significance value isa minimum distance parameter, for example. The minimum distanceparameter is a minimum distance from the origin, for example.

In various embodiments, method 200 can also include assigning adifferent symbol to each group that is identified. These symbols canthen be used to visualize and interpret the loadings data.

In various embodiments, method 200 can also include assigning a set ofvariables that are anti-correlated to a group. This includes extending aline including the vector on an opposite side of the origin of thesubset PC space, selecting a second set of one or more variables withinthe spatial angle of the line on the opposite side of the origin, andadding the second set to the group, if the set and the second setincludes the minimum number of variables. In various embodiments, if oneor more variables within the second set have a significance value lessthan a threshold value, then the one or more variables are not selectedfor the second set. The significance value is a minimum distanceparameter, for example. The minimum distance parameter is a minimumdistance from the origin, for example.

In various embodiments, method 200 can also include removing the setfrom further analysis, selecting a second variable in the PC space,selecting a second set of one or more variables within the spatial angleof a second vector extending from the origin of the subset PC space tothe second variable, and assigning the second set to a second group ofvariables, if the second set includes the minimum number of variables.The second group identifies correlated variables also. The minimumnumber of variables can be, for example, one or a number greater thanone. The second variable can, for example, be the unassigned variablethat is furthest from the origin of the subset PC space.

In various embodiments, method 200 can also include calculating a thirdvector from the second group, selecting a third set of one or morevariables within the spatial angle of the third vector; and replacingthe variables of the second group with the variables of the third set,if the third set includes a minimum number of variables. The variablesof the second group are assigned from the second set, for example. Thethird vector can be any linear or nonlinear combination of the variablesin the second group. For example, the third vector can be, but is notlimited to, the arithmetic mean, a weighted mean, the median, or thegeometric mean. In various embodiments, one or more variables within thethird set that have a significance value less than a threshold value arenot selected. The significance value is a minimum distance parameter,for example. The minimum distance parameter is a minimum distance fromthe origin, for example. For visualization and interpretation purposes,a second and different symbol can be assigned to the second group.

In various embodiments, method 200 can also include assigning a set ofvariables that are anti-correlated to the second group. This includesextending a line comprising the second vector on an opposite side of theorigin, selecting a third set of one or more variables within thespatial angle of the line on the opposite side of the origin, and addingthe third set to the second group, if the set and the third set includethe minimum number of variables. The minimum number of variables can be,for example, one or a number greater than one. In various embodiments,if one or more variables within the third set that have a distance fromthe origin less than a threshold value, then the one or more variablesare not selected. The threshold value is a minimum distance parameter,for example.

In various embodiments, method 200 can also include sorting assignedgroups. The sorting can be done, for example, by the largest distancefrom the origin in each group.

In various embodiments, method 200 can also include removing variablesassigned to the group in step 260 from further analysis and repeatingthe steps of removing variables of a last assigned group from furtheranalysis, selecting a new variable in the subset PC space, selecting anew set of one or more variables within the spatial angle of a newvector extending from the origin to the new variable, assigning the newset to a new group, if the new set includes the minimum number ofvariables, and removing variables of the new group from further analysisuntil the variables not assigned to a group do not exceed a threshold.The threshold can be, for example, a distance from the origin. Repeatingthese steps produces a plurality of groups of correlated variables, forexample.

As mentioned above, PCA can be applied to data with a large number ofvariables and comparatively few samples (this data is said to have highdimensionality). Other analysis techniques require data where the numberof samples exceeds the number of variables. Examples of these otheranalysis techniques include, but are not limited to, linear discriminantanalysis (LDA) and independent component analysis (ICA). PCA, therefore,can be used to reduce the dimensionality of data for use in otheranalysis techniques, such as LDA and ICA. The reduced dimensions can bePCs or group representations of the groups. Using group representationsis preferable, because groups are interpretable combinations of theoriginal variables.

In various embodiments, method 200 can also include assigning a grouprepresentation to the group and using the group representation and theplurality of samples as input to a subsequent analysis technique. Thegroup representation can include, but is not limited to, the mostintense variable of the group, a variable with the mean intensity of thegroup, or the sum of the variables of the group. The subsequent analysistechnique can include, but is not limited to, a clustering technique ora pattern recognition technique. The subsequent analysis technique caninclude, but is not limited to, LDA or ICA.

In various embodiments, method 200 can also include processing the grouprepresentation to generate new variables for input to the subsequentanalysis technique. The subsequent analysis technique can include, butis not limited to, LDA, ICA, or PCA. Processing the group representationcan include, but is not limited to, generating a nonlinear combinationof the group representation and at least one other group representation.For example, a new variable can be a ratio of the group representationand another group representation.

In various embodiments of the present teachings, data scaling isperformed prior to PCA processing so that, for example, high intensityvariables do not dominate the analysis. One scaling technique isautoscaling, where the value for each variable is processed by firstsubtracting the mean of all values of the variable (i.e., meancentering) and then dividing by the variance of the variable.Autoscaling weights all variables equally and is appropriate where thevariables are unrelated and can have widely different scales. However,when the variables are all of the same type (i.e., mass spectral orchromatographic peaks) and the more intense variables are moresignificant and less likely to be noise, Pareto scaling can be moreadvantageous. In Pareto scaling the mean centered values are divided bythe square root of the variance. Pareto scaling reduces, but does noteliminate, the original intensity contribution and helps in interpretingloadings plots.

FIG. 3 is an exemplary illustration 300 that shows how a set of one ormore variables 340 can be found within a spatial angle 350 of a selectedvariable 360, in accordance with the present teachings. Thethree-dimensional PC space shown in FIG. 3 includes PCs PC1 310, PC2320, and PC3 330. Variable 360 is selected in this three-dimensional PCspace. Spatial angle 350 is defined around a vector extending from theorigin to selected variable 360. One or more variables found withinspatial angle 350 are selected as the set of one or more variables 340.

FIG. 4 is an exemplary schematic diagram showing a computing system 400for grouping variables after PCA of a plurality of variables from aplurality of samples produced by a measurement technique that isconsistent with the present teachings. Computing system 400 includesgrouping module 410. Grouping module 410 selects the number of PCsproduced by the PCA, creates a subset PC space having the number of PCs,selects a variable, defines a spatial angle around a vector extendingfrom an origin to the variable, selects a set of one or more variableswithin the spatial angle of the vector, and assigns the set to a group,if the set includes a minimum number of variables.

In various embodiments of computing system 400, the plurality ofvariables can be generated using a measurement technique that generatesmore than one variable per constituent of a sample. The plurality ofvariables is generated using a measurement device, for example, as shownin FIG. 15. A measurement device can be, but is not limited to, aspectrometer or a mass spectrometer. Measurement techniques can include,but are not limited to, nuclear magnetic resonance, infra-redspectrometry, near infra-red spectrometry, ultra-violet spectrometry,Raman spectrometry, or mass spectrometry. In various embodiments theplurality of variables can be generated using a measurement techniquethat generates more than one variable per constituent of a samplecombined with a separation technique. Separation techniques can include,but are not limited to, liquid chromatography, gas chromatography, orcapillary electrophoresis.

In various embodiments, grouping module 410 can also select a secondvariable in the PC space, select a second set of one or more variableswithin the spatial angle of a second vector extending from the origin tothe second variable, and assign the second set to a second group ofvariables, if the second set comprises the minimum number of variables.

Another PCVG method consistent with the present teachings is outlinedbelow:

-   -   1. Perform PCA on all variables using Pareto scaling.    -   2. Determine the number of PCs (m) to be used. Using all n of        the PCs extracted will exactly reproduce the original data.        However, many of these PCs represent noise fluctuations in the        data and can be ignored with no loss of information. Selecting m        PCs effectively smoothes the data. Each variable is represented        by a vector in this m-dimensional space.    -   3. Determine the target vector (t) that corresponds to the        variable furthest from the origin. For this to be effective        autoscaling is not used. Autoscaling is undesirable because it        weights all variables, including small noise peaks, equally.    -   4. Define a spatial angle (α) around this vector and find other        data points (vectors) that are within that angle, optionally        ignoring low intensity variables. If a second vector is x, then        the angle (θ) between x and the target vector can be found from:

x·t=|x∥t|cos(θ)

-   -   5. Calculate the mean of all selected vectors and repeat step 3        using the new mean vector and assign all selected variables to a        group. “Re-centering” in this way fine tunes the orientation of        the spatial angle and can be effective if the most intense        variable is atypical in some way. For example, the profile may        be distorted if the peak is saturated in the most concentrated        samples. Since Pareto scaling has been used, calculating the        mean vector also causes the lower intensity ions to have less        effect on the result.    -   6. Repeat the process from step 3 ignoring previously grouped        variables until there are no remaining variables with sufficient        intensity.

FIG. 5 is an exemplary flowchart showing a computer-implemented method500 that can be used for processing data in n-dimensional space and thatis consistent with the present teachings.

In step 510 of method 500, PCA is performed on all variables and thespecified subset of PCs is used.

In step 520, variables with low significance are removed. Filtering outvariables that have low significance with respect to the selectedscaling and PCA significance measure is optional. The same effect can beachieved by adding a step after grouping the variables and by using adifferent significance criterion. Another significance criterion thatcan be used is optical contrast, for example.

In step 530, a vector of an unassigned variable furthest from the originis found.

In step 540, all vectors within a spatial angle of the vector are found.

In step 550, a mean of vectors within a spatial angle of the vector isfound.

In step 560, all unassigned variables within the spatial angle of themean are found and assigned to a group. Variables assigned to the groupare then removed from processing.

In step 570, if any variables are left for processing, method 500returns to step 530. If no variables are left for processing, method 500ends.

The result of this processing is a number of groups of correlatedvariables that can be interpreted further, or group representations thatcan be used as input to subsequent analysis techniques. Forvisualization purposes, it is useful to identify grouped variables in aloadings plot by assigning a symbol to the group. Interpretation can beaided by generating intensity or profile plots for all members of agroup.

Iterative Principal Component Variable Grouping

As described above, mass spectrometry's ability to generate largeamounts of data poses a significant problem for many data processingtechniques. In particular, the high dimensionality (large number ofvariables) of mass spectrometry (MS) data with a large number ofsamples, liquid chromatography coupled mass spectrometry (LC-MS) data,and imaging MS data can be a problem for these techniques.

The large number of variables produced by MS can also be a problem forprincipal component analysis (PCA) followed by variable grouping, orprincipal component variable grouping (PCVG). PCVG can be affected by alarge number of variables in at least two different ways. First, a largenumber of variables can overwhelm the processor or computer used toperform the PCVG algorithm. As result, data analysis cannot be performedin a reasonable period of time. Second, a large number of variables canreduce the specificity of the PCVG algorithm decreasing the quality ofresults. For example, when processing large amounts of data, smallerpieces of the data can be obscured by the overall noise of the largedata set.

In various embodiments, PCVG is applied iteratively to segments of thedata in order to handle a large amount of data. In this technique, thedata is judiciously divided into segments. The segments are chosen sothat they are small enough not to reach the limitations of the computeror processor used to execute the PCVG algorithm and not to cause areduction in the specificity of the PCVG results. The segments are alsochosen so that they are large enough so that PCVG can produce a numberof correlated groups without having to perform too many iterations.

In order to reduce the overall amount of data, each group of correlatedvariables produced by performing PCVG on a segment is replaced with agroup representation. Consequently, the result of performing PCVG on allsegments is data set of all of the group representations produced by allof the segments. If the total number of all of the group representationsis still too large for a single PCVG run, the data set of all of thegroup representations is divided again into segments and PCVG isperformed on each segment. This division of group representationsfollowed by iterations of PCVG continues until the total number of allof the group representations is small enough to allow a single run ofPCVG that will perform within the constraints of the processor used andwill provide the required specificity.

Once the total number of all of the group representations is smallenough to allow a single run of PCVG, PCVG is performed on the data setof all of the group representations and groups of correlated variablesare identified. These groups represent the correlated variables for theoriginal large amount of measured MS data.

FIG. 15 is a schematic diagram showing a system 1500 for identifyinggroups of correlated representations of variables from a large amount ofspectrometry data, in accordance with the present teachings. System 1500includes spectrometer 1510 and processor 1520. Spectrometer 1510 is amass spectrometer, for example. Processor 1520 can be, but is notlimited to, a computer, microprocessor, or any device capable of sendingand receiving control signals and data from spectrometer 1510 andprocessing data. Spectrometer 1510 analyzes a plurality of samples andproduces a plurality of variables from the plurality of samples.

Processor 1520 is in communication with spectrometer 1510. Processor1520 performs a number of steps.

(1) Processor 1520 obtains the plurality of measured variables fromspectrometer 1510 and divides the plurality of measured variables into aplurality of measured variable subsets.

(2) Processor 1520 performs PCVG on each measured variable subset,producing one or more group representations for each measured variablesubset and a plurality of group representations for the plurality ofmeasured variable subsets.

(3) Processor 1520 calculates a total number of the plurality of grouprepresentations as the sum of the number of the one or more grouprepresentations produced for each measured variable subset.

(4) If the total number is less than or equal to a maximum number ofvariables allowed for principal component analysis followed by variablegrouping, processor 1520 jumps to step (10). The maximum number is basedon the processing power of processor 1520, for example. In variousembodiments, the maximum number is based on the number of points neededso that correlated points are not broken into different subsets.

(5) Processor 1520 divides the plurality of group representations into aplurality of group representation subsets.

(6) Processor 1520 performs PCVG on each group representation subset,producing one or more group representations for each grouprepresentation subset and a plurality of group representations for theplurality of group representation subsets.

(7) Processor 1520 calculates the total number of the plurality of grouprepresentations as a sum of the number of the one or more grouprepresentations produced for each group representation subset.

(8) If the total number is less than or equal to a maximum number,processor 1520 jumps to step (10).

(9) If the total number is greater than the maximum number of variables,the processor repeats steps (5)-(9), and

(10) Processor 1520 performs PCVG on the plurality of grouprepresentations, producing a plurality of groups of correlatedrepresentations of variables.

In various embodiments, processer 1520 performs PCVG on each measuredvariable subset in step (2) according to the following steps.

(i) Processor 1520 performs principal component analysis on eachmeasured variable.

(ii) Processor 1520 selects a number of principal components produced bythe principal component analysis,

(iii) Processor 1520 creates a subset principal component space havingthe number of principal components.

(iv) Processor 1520 selects a variable of the each measured variablesubset in the subset principal component space that has a significancevalue greater than a threshold value. The threshold value is a minimumdistance from the origin of the subset principal component space, forexample.

(v) Processor 1520 defines a spatial angle around a vector extendingfrom the origin to the variable.

(vi) Processor 1520 selects a group of one or more variables within thespatial angle of the vector.

(vii) Processor 1520 assigns a group representation to the group, if thegroup comprises a minimum number of variables. The minimum number ofvariables is a minimum number of correlated variables a group isexpected to include, for example.

(viii) Processor 1520 repeats steps (iv)-(viii) until no variablesremain in the subset principal component space that have not beenselected, that have not been made part of a group to which a grouprepresentation has been assigned, or that have a significance value thatexceeds the threshold value.

In various embodiments, the spatial angle defined in step (v) byprocessor 1520 is a constant angle for applications. This constant angleis approximately 15 degrees, for example. In various embodiments, thenumber of principal components selected in step (ii) by processor 1520is adjusted for different applications. The number of principalcomponents is selected so that the number of variable groups is smallerthan the expected maximum number of independent components, for example.

Similarly and in various embodiments, processer 1520 performs PCVG oneach group representation subset in step (6), as described above,according to the following steps.

(i) Processor 1520 performs principal component analysis on each grouprepresentation subset.

(ii) Processor 1520 selects a number of principal components produced bythe principal component analysis.

(iii) Processor 1520 creates a subset principal component space havingthe number of principal components.

(iv) Processor 1520 selects a representation of each grouprepresentation subset in the subset principal component space that has asignificance value greater than a threshold value. The threshold valueis a minimum distance from the origin of the subset principal componentspace, for example.

(v) Processor 1520 defines a spatial angle around a vector extendingfrom an origin of the subset principal component space to therepresentation.

(vi) Processor 1520 selects a group of one or more representationswithin the spatial angle of the vector.

(vii) Processor 1520 assigns a group representation to the group, if thegroup comprises a minimum number of representations. The minimum numberof representations is a minimum number of correlated representations agroup is expected to include, for example.

(viii) Processor 1520 repeats steps (iv)-(viii) until no representationsremain in the subset principal component space that have not beenselected, that have not been made part of a group identified as a groupof correlated representations, or that have a significance value thatexceeds the threshold value.

Finally and in various embodiments, processer 1520 performs PCVG on theplurality of group representations in step (10), as described above,according to the following steps.

(i) Processor 1520 performs principal component analysis on theplurality of group representations.

(ii) Processor 1520 selects a number of principal components produced bythe principal component analysis.

(iii) Processor 1520 creates a subset principal component space havingthe number of principal components.

(iv) Processor 1520 selects a representation of the plurality of grouprepresentations in the subset principal component space that has asignificance value greater than a threshold value. The threshold valueis a minimum distance from the origin of the subset principal componentspace, for example.

(v) Processor 1520 defines a spatial angle around a vector extendingfrom an origin of the subset principal component space to therepresentation.

(vi) Processor 1520 selects a group of one or more variables within thespatial angle of the vector.

(vii) Processor 1520 identifying the group as a group of correlatedrepresentations of variables, if the group comprises a minimum number ofrepresentations. The minimum number of representations is a minimumnumber of correlated representations of variables a group is expected toinclude, for example.

(viii) Processor 1520 repeats steps (iv)-(viii) until no representationsremain in the subset principal component space that have not beenselected, that have not been made part of a group identified as a groupof correlated representations of variables, or that have a significancevalue that exceeds the threshold value.

FIG. 16 is a flowchart showing a method 1600 for identifying groups ofcorrelated representations of variables from a large amount ofspectrometry data, in accordance with the present teachings.

In step 1605 of method 1600, a plurality of samples is analyzed using aspectrometer. The plurality of samples is analyzed using measurementstechniques including, but not limited to, mass spectrometry (MS), liquidchromatography coupled mass spectrometry (LC-MS), or imaging massspectrometry

In step 1610, a plurality of measured variables is produced from theplurality of samples using the spectrometer.

In step 1615, the plurality of measured variables is obtained from thespectrometer using a processor.

In step 1620, the plurality of measured variables is divided into aplurality of measured variable subsets using the processor.

In step 1625, PCVG is performed on each measured variable subset usingthe processor, producing one or more group representations for eachmeasured variable subset and a plurality of group representations forthe plurality of measured variable subsets.

In step 1630, a total number of the plurality of group representationsis calculated as a sum of the number of the one or more grouprepresentations produced for each measured variable subset using theprocessor.

In step 1635, it is determined if the total number is less than or equalto the maximum number of variables allowed for PCVG using the processor.If the total number is less than or equal to the maximum number, method1600 jumps to step 1680 using the processor.

In step 1640, the plurality of group representations is divided into aplurality of group representation subsets using the processor.

In step 1645, PCVG is performed on each group representation subsetusing the processor, producing one or more group representations foreach group representation subset and a plurality of grouprepresentations for the plurality of group representation subsets.

In step 1650, the total number of the plurality of group representationsis calculated as a sum of the number of the one or more grouprepresentations produced for each group representation subset using theprocessor.

In step 1655, it is determined if the total number is greater than themaximum number of variables using the processor. If the total number isgreater than the maximum number, method 1600 jumps to step 1640.

In step 1660, PCVG is performed on the plurality of grouprepresentations using the processor, producing a plurality of groups ofcorrelated representations of variables.

In various embodiments, a computer program product includes a tangiblecomputer-readable storage medium whose contents include a program withinstructions being executed on a processor so as to perform a method foridentifying groups of correlated variables from a large amount of data.This method is performed by a system of distinct software modules.

FIG. 17 is a schematic diagram of a system 1700 of distinct softwaremodules that performs a method for identifying groups of correlatedrepresentations of variables from a large amount of spectrometry data,in accordance with the present teachings. System 1700 includesmeasurement module 1710, segmentation module 1720, and grouping module1730.

Measurement module 1710 obtains a plurality of variables from aplurality of samples produced by a spectrometric measurement technique.The spectrometric measurement technique can include, but is not limitedto, mass spectrometry (MS), liquid chromatography coupled massspectrometry (LC-MS), or imaging mass spectrometry. Segmentation module1720 divides the plurality of measured variables into a plurality ofmeasured variable subsets.

Grouping module 1730 performs a number of steps.

(1) Grouping module 1730 performs PCVG on each measured variable subsetusing the grouping module, producing one or more group representationsfor each measured variable subset and a plurality of grouprepresentations for the plurality of measured variable subsets

(2) Grouping module 1730 calculates a total number of the plurality ofgroup representations as a sum of a number of the one or more grouprepresentations produced for each measured variable subset.

(3) If the total number is less than or equal to a maximum number ofvariables allowed for PCVG, grouping module 1730 jumps to step (9).

(4) Grouping module 1730 divides the plurality of group representationsinto a plurality of group representation subsets.

(5) Grouping module 1730 performs PCVG on each group representationsubset, producing one or more group representations for each grouprepresentation subset and a plurality of group representations for theplurality of group representation subsets.

(6) Grouping module 1730 calculates the total number of the plurality ofgroup representations as a sum of a number of the one or more grouprepresentations produced for each group representation subset.

(7) If the total number is less than or equal to the maximum number,grouping module 1730 jumps to step (9).

(8) If the total number is greater than the maximum number of variables,grouping module 1730 steps (4)-(8).

(9) Grouping module 1730 performs principal component analysis followedby variable grouping on the plurality of group representations,producing a plurality of groups of correlated representations ofvariables.

Aspects of the present teachings may be further understood in light ofthe following examples, which should not be construed as limiting thescope of the present teachings in any way.

Software Example

FIG. 6 is an exemplary image of a user interface 600 for a software toolto perform variable grouping, in accordance with the present teachings.User interface 600 and the software tool can be used with existingviewing programs. One existing viewing program is, for example,MARKERVIEW™ from Applied Biosystems/MDS Sciex.

The software tool can be run while an existing viewing program isrunning and after some data has been processed to generate scores andloadings plots. On starting, the software tool can interrogate theviewing program and obtain the loadings data. Following processing, thesoftware tool can set a “group” column in the viewing program's loadingstable so that the data points can be assigned symbols.

The number of PCs can be selected in three ways. First the number of PCscan be based on those currently displayed in the loadings plot bychoosing selection 610. Second, a specific number of PCs can be enteredusing selection 620. Third, the software tool can select a number of PCsthat explains a given amount of variance using selection 630. Selectinga number of PCs that represents a given amount of variance allows somecontrol of the amount of noise ignored.

In field 640 of user interface 600, a user can enter a spatial angleparameter. In field 650, a user can enter a minimum intensity or minimumdistance from the origin parameter. If desired, using “exclude small”button 660 on user interface 600, variables less than the minimumdistance from the origin parameter can be marked as excluded so thatthey will not be used in any subsequent analysis.

Automatic or manual grouping can be selected using selection 665 fromuser interface 600. In the manual case, a user can select a variable ofinterest in the loadings plots and the software tool extracts a singlegroup using that variable as the starting point. Selecting automaticprocessing, using selection 665 on user interface 600, allows a user toenter an additional threshold in field 670 for starting a group, whichmeans that small variables can be considered if they are assigned to agroup containing a larger variable, but small variables cannot be usedto start a new group. User interface 600 can also include field 675 thatrequires a group to contain a minimum number of variables. Field 675 canbe used if the data is expected to contain a number of correlatedvariables.

As described previously, correlated variables will lie substantially onthe same straight line and will be on the same side of the origin of theloadings plot. The software tool can optionally include in the samegroup variables that are close to the extension of the line on theopposite side of the origin. These variables are anti-correlated.Inclusion of correlated and anti-correlated groups can be selected usingselection 680 from user interface 600.

Finally, using selection 685 of user interface 600, a user can select tohave the assigned groups sorted based on the intensity of the startingvariable or based on the closeness in m-dimensional space to the firstvariable, for example.

Although user interface 600 shows three ways (i.e., selections 610, 620,and 630) of selecting the number of PCs, a software tool can use anyknown algorithm to determine how many are significant. In fact, theapproach described in the present teachings can be used to iterativelydetermine the number of PCs to use and the groups. Typically increasingthe number of PCs has little effect until the PCs are mostly due tonoise, which can cause the number of groups to jump dramatically. As aresult, the number of PCs used can be limited to a value less than thevalue causing the jump in the number of groups.

Data Examples

In various embodiments of the present teachings, methods are describedfor analyzing PC loadings to determine related variables. For example,those showing similar expression patterns from a series of samples.These methods are illustrated using mass spectrometry (MS) data.However, these methods are applicable to other applications.

The data can be generated by analyzing each sample using a variety ofspectrometric techniques, such as nuclear magnetic resonance (NMR),infra-red spectrometry (IR), near infra-red spectrometry (NIR),ultra-violet spectrometry (UV), Raman spectrometry, or mass spectrometry(MS). Analyses may also be performed using hyphenated techniques thatcouple one of the above spectrometric techniques with a chromatographicseparation, such as liquid chromatography (LC), gas chromatography (GC),or capillary electrophoresis (CE). An exemplary hyphenated technique isliquid chromatography mass spectrometry (LC-MS). The patterns may be dueto real biological variation that is of interest, such as changes due todisease or treatment with a therapeutic, or may be artifacts of theanalysis that can be ignored. The variables found to be related can beinterpreted to determine the compounds causing the pattern.

Another exemplary application for these methods can be finding peaks indata from a hyphenated technique. The data is generated using anexemplary hyphenated technique listed above by collecting a series ofspectra from the effluent of a separation process. The patterns are dueto the intensity profiles observed as peaks elute from the separation.Related variables will have the same pattern of variation andoverlapping (unresolved) peaks can be determined. The variables found tobe related can be interpreted to determine the compounds causing thepattern.

Another exemplary application for these methods can be interpretingtissue image data. The data is generated by any techniques that can givemultiple measurements, such as a spectrum, at various points across asample of biological tissue. The patterns are due to variations in theamount of compounds at different parts of the tissue and may correspondto different features or structures, such as organs and organelles. Thevariables found to be related can be interpreted to determine thecompounds causing the pattern.

For MS data, the variables in the columns of the Input Matrix aregenerally mass bins or centroid values, for liquid chromatographycoupled mass spectrometry (LC-MS) the variables are characterized bymass-to-charge ratios (m/z) and retention time. In both cases, the datais aligned to ensure that the variable refers to the same signal in allsamples.

FIG. 7 is an exemplary scores plot 700 of two PCs for MS spectra dataobtained after Pareto scaling and PCA, in accordance with the presentteachings. The MS spectra data shown in FIGS. 7-9 was obtained usingmatrix-assisted laser desorption/ionization (MALDI). MALDI MS spectradata can be obtained, for example, using a mass spectrometer such as theAPPLIED BIOSYSTEMS/MDS SCIEX TOF/TOF™ time of flight/time of flight massspectrometer. PCA analysis and visualization of MALDI MS spectra datacan be performed, for example, using MARKERVIEW™ software from AppliedBiosystems/MDS Sciex.

FIG. 7 shows scores for samples from a protein digest with and without aspike of calibration mixture. Scores with a spike of a calibrationmixture are shown with symbol 710 in FIG. 7. Scores without a spike of acalibration mixture are shown with symbol 720 in FIG. 7. Labels shown inFIG. 7 with symbols 710 and 720 are a combination of sample and samplegroup names.

As shown in FIG. 7, the spiked 710 samples and unspiked 720 samples arecleanly separated by the first PC, PC1, which explains the largestamount of variance. The spiked 710 samples have larger PC1 scores,indicating that they have relatively more of the variables with large,positive loadings, as shown in FIG. 8, than the unspiked 720 samples.

FIG. 8 is an exemplary loadings plot 800 of two PCs for MS spectra dataobtained after Pareto scaling and PCA, in accordance with the presentteachings. The labels in plot 800 correspond to the centroid m/z valueof the variable.

In the example shown in FIG. 8, variables with the largest PC1 loadingstend to lie on straight line 810 that passes through the origin of theplot. This feature arises because these variables are correlated andshow the same behavior across the sample set.

FIG. 8 also shows one benefit of Pareto scaling in interpreting theloadings plot. For any particular isotope cluster, the distance from theorigin reflects the relative intensity of the peak. Thus it can bedetermined if the members of an isotope cluster have the same behavioras expected, which increases confidence in the observedseparation/correlation.

FIG. 9 is an exemplary profile plot 900 of a few representativevariables 910 from MS spectra data obtained after Pareto scaling andPCA, in accordance with the present teachings. A profile plot is a plotof the response of one or more variables as a function of a plurality ofsamples. Note that the correlation for variables 910 in FIG. 9 is notperfect due to noise. The slight variation in profiles causes thescatter around correlation line 810 shown in FIG. 8.

In various embodiments, components of a peak can be determined using amultivariate analysis technique on the data from a collection ofspectra. If the peak contains data points that have different behaviorsacross the collection of spectra, the peak is determined to be aconvolved peak.

FIG. 10 is a flowchart showing a method 1000 for identifying a convolvedpeak, in accordance with the present teachings.

In step 1010 of method 1000, a plurality of spectra is obtained. Theplurality of spectra is obtained from multiple samples, for example. Invarious embodiments, the plurality of spectra is obtained from a singlesample. In various embodiments, obtaining the plurality of spectra caninclude, but is not limited to, performing spectroscopy, massspectrometry, or nuclear magnetic resonance spectrometry.

In step 1020, a multivariate analysis technique is used to assign datapoints from the plurality of spectra to a plurality of groups.

In step 1030, a peak is selected from the plurality of spectra.

In step 1040, if the peak includes data points assigned to two or moregroups of the plurality of groups, the peak is identified as a convolvedpeak.

In various embodiments of method 1000, the multivariate analysistechnique can include an unsupervised clustering algorithm. Anunsupervised clustering algorithm can include, but is not limited to, aself-organizing map, a k-means clustering algorithm, or a hierarchicalclustering algorithm.

An unsupervised clustering algorithm can also include performingprincipal component analysis on the data points and using a method foridentifying correlated data points after the principal componentanalysis to assign the data points to the plurality of groups. A numberof principal components produced by the principal component analysis canbe selected. A subset principal component space having the number ofprincipal components can be created. A data point in the subsetprincipal component space can be selected. A vector can be extended froman origin of the subset principal component space to the data point. Oneor more data points in the subset principal component space and within aspatial angle around the vector can be identified as a group ofcorrelated data points. The group of correlated data points can then beassigned to the plurality of groups.

In various embodiments, method 1000 can also include processing one ormore groups of the two or more groups of the plurality of groups toobtain information about a component of the peak. This information caninclude, but is not limited to, intensity data, mass data, chemicalshift data, or wavelength data.

In various embodiments, method 1000 can be used with any spectroscopictechnique and sample collection method.

In various embodiments, method 1000 can also include obtaining theplurality of spectra from analysis techniques including, but not limitedto, liquid chromatography mass spectrometry analysis, gas chromatographymass spectrometry analysis, capillary electrophoresis mass spectrometryanalysis, super-critical fluid chromatography mass spectrometryanalysis, ion mobility mass spectrometry analysis, field asymmetric ionmobility mass spectrometry analysis, liquid chromatography nuclearmagnetic resonance analysis, liquid chromatography ultravioletspectroscopic analysis, gas chromatography infrared spectroscopicanalysis, or spatial analysis.

In various embodiments, related data points can be determined byanalyzing a number of samples. The related data points can be determinedif they are correlated across the number of samples. For example, if thedata points are part of a profile spectrum, a spectral peak may be foundthat appears to be a singlet, but actually has components that behavedifferently.

The samples may be a series of single spectra from a collection of real,physical samples. The spectra may be measured directly or obtained bycombining all the spectra from the LCMS analyses of individual samples.The samples may be a series of spectra from the same sample, forexample, spectra obtained across an LCMS peak. It is important thatthere is some variation of the ratio of the components of the convolvedpeaks among the spectra, but the exact form does not have to be known.

FIG. 11 is an exemplary plot 1100 of a convolved peak 1110 from aspectrum, in accordance with the present teachings. The differentsymbols 1120, 1130, and 1140 correspond to data points of differentgroups assigned using a method for grouping variables after principalcomponent analysis. The spectrum was obtained from a single sample, butthe groups were determined by using the spectra from a number of samplesto reveal different parts of each peak that have correlated behaviors.

FIG. 12 is an exemplary plot 1200 of how intensity for each mass of afirst group 1220 varies across samples, in accordance with the presentteachings. The first group 1220 corresponds to symbols 1120 shown inFIG. 11.

FIG. 13 is an exemplary plot 1300 of how intensity for each mass of asecond group 1330 varies across samples, in accordance with the presentteachings. The second group 1330 corresponds to symbols 1130 shown inFIG. 11.

FIG. 14 is an exemplary plot 1400 of how intensity for each mass of athird group 1440 varies across samples, in accordance with the presentteachings. The third group 1440 corresponds to symbol 1140 shown in FIG.11.

A profile plot shows the response of a data point across samples. Plot1200 in FIG. 12, plot 1300 in FIG. 13, and plot 1400 in FIG. 14 areprofile plots of data points corresponding to symbols 1120, 1130, and1140 in FIG. 11, respectively. Plot 1200 in FIG. 12 corresponding tosymbols 1120 in FIG. 11 depicts a profile that is different from plot1300 in FIG. 13 corresponding to symbols 1130 in FIG. 11. Data pointsrepresented by symbols 1120 and 1130 in FIG. 11 are present in allsamples of plot 1200 in FIG. 12 and plot 1300 in FIG. 13, respectively,but show more intense values in particular samples. This indicates thatthey in fact belong to separate compounds.

The data point represented by symbol 1140 in FIG. 11 and plotted acrosssamples in plot 1400 of FIG. 14 shows that this data point is likelypresent in the compound corresponding to the data points represented bysymbol 1120 in FIG. 11 and the compound corresponding to the data pointsrepresented by symbol 1130 in FIG. 11, since plot 1400 of FIG. 14represents a sum of plot 1200 of FIG. 12 and plot 1300 of FIG. 13. Hencethe third group 1430 of FIG. 14 is a separate group but does notindicate the presence of an additional compound. Thus the groupsassociated with the same peak must be processed to determine the actualnumber of compounds present.

Processing Fragment Ion Spectra

As described above, fragment ion spectra can support a defined mechanismor propose novel mechanisms of fragmentation and assist in thedetermination of the structure of a molecule. However, it is typical tothreshold fragment ion spectra prior to analysis. As a result,potentially important information is lost.

In various embodiments, by the use of a collision energy (CE) ramp,either performed intra or inter scan, decomposition profiles for thecompound, or molecule, can be generated. This data is then treated suchthat principal component analysis (PCA) analysis can be performedfollowed by principal component variable grouping (PCVG). PCA-PCVGanalysis provides insight into the ions that are being created andfragmented with respect to CE. By mapping these fragments it is possibleto formulate a series of groups of ions which could be associated witheach other (i.e. ions which show a CE maxima of 35 ev would be generatedafter a group of ions which show a maximum at 25 ev).

The ions in the different groups are not limited to the major ions. Invarious embodiments, a minor ion can also be accounted for by comparingits fragmentation profile to that of the other group members. It ispossible that the minor ions could then be linked to formulae dependentupon the order by which they are generated. Grouping based on CE canthen also be sub grouped based on their mass (i.e. a fragment of 405 m/zwith a CE maxima of 35 ev could not be generated from a fragment with am/z less than 405 in a group with a CE maxima less than 35 ev). Suchgrouping allows a cluster model to be defined from the formation of thefragments based on their mass and also their CE maxima.

By determining the peak shape and also the peak intensity another levelof information can be determined. The peak shape across the CE ramp isindicative of the rate of formation and decay. If there is more than onepossible route to forming the mass, then the peak width will bedifferent from other peaks. This can be used to show a difference in thefragmentation pathways. Besides the peak shape the intensity of the peakcan also be used to perform a series of mass balance measurements on thesuspected related fragment ions.

Systems and Methods of Data Processing Mass Spectrometry System

FIG. 18 is a schematic diagram showing a system 1800 for groupingfragment ions of a molecule using tandem mass spectrometry with ramps incollision energy, in accordance with various embodiments. System 1800mass spectrometer 1810 and processor 1820.

Mass spectrometer 1810 is a tandem mass spectrometer, for example. Massspectrometer 1810 can include one or more physical mass analyzers thatperform two or more mass analyses. A mass analyzer of a tandem massspectrometer can include, but is not limited to, a time-of-flight (TOF),quadrupole, an ion trap, a linear ion trap, an orbitrap, a magneticfour-sector mass analyzer, a hybrid quadrupole time-of-flight (Q-TOF)mass analyzer, or a Fourier transform mass analyzer. Mass spectrometer1810 can include separate mass spectrometry stages or steps in space ortime, respectively.

Mass spectrometer 1810 fragments and analyzes a known molecule using aplurality of different collision energies. Mass spectrometer 1810produces a plurality of variables for the plurality of fragment ions.

Processor 1820 is in communication with mass spectrometer 1810.Processor 1820 can also be in communication with separation device 1810.Processor 1820 can be, but is not limited to, a computer,microprocessor, or any device capable of sending and receiving controlsignals and data to and from mass spectrometer 1810 and processing data.

Processor 1820 obtains the plurality of variables from mass spectrometer1810. Processor 1820 can obtain the plurality of variables in real timeor read the plurality of variables from a file stored in memory, forexample. Processor 1820 performs principal component analysis on theplurality of variables. Processor 1820 selects a number of principalcomponents produced by the principal component analysis. Processor 1820creates a subset principal component space having the number ofprincipal components. Processor 1820 selects a variable in the subsetprincipal component space. Processor 1820 defines a spatial angle arounda vector extending from the origin to the variable. Processor 1820selects a set of one or more variables within the spatial angle of thevector. Processor 1820 assigns the set to a group, if the set comprisesa minimum number of variables. The group identifies correlatedvariables. The minimum number of variables is the number of correlatedvariables a group is expected to include, for example.

In various embodiments, processor 1820 creates one or more additionalgroups. Processor 1820 removes the set of one or more variables in eachgroup from further analysis after the creation of each group. Processor1820 then repeats the above mentioned steps starting with selecting avariable in the subset principal component space, until remainingvariables not assigned to a group do not exceed a threshold. A pluralityof groups is created.

In various embodiments, processor 1820 orders the plurality of groups bycollision energy.

In various embodiments, processor 1820 maps one or more groups of theplurality of groups to a fragmentation pathway.

In various embodiments, processor 1820 creates a profile plot thatincludes a fragment ion from each group of the plurality of groups.

In various embodiments, processor 1820 compares peak shapes and/orintensities of the profile plot to map a fragment ion to a fragmentationpathway.

Mass Spectrometry Method

FIG. 19 is an exemplary flowchart showing a method 1900 for groupingfragment ions of a molecule using mass spectrometry with ramps incollision energy, in accordance with various embodiments.

In step 1910 of method 1900, a plurality of variables for a plurality offragment ions produced by a mass spectrometer that fragments andanalyzes a known molecule using a plurality of different collisionenergies are obtained.

In step 1920, principal component analysis is performed on the pluralityof variables.

In step 1930, a number of principal components produced by the principalcomponent analysis are selected.

In step 1940, a subset principal component space having the number ofprincipal components is created.

In step 1950, a variable in the subset principal component space isselected.

In step 1960, a spatial angle around a vector extending from the originto the variable is defined.

In step 1970, a set of one or more variables within the spatial angle ofthe vector is selected.

In step 1980, the set is assigned to a group, if the set comprises aminimum number of variables. The group identifies correlated variables.The minimum number of variables is the number of correlated variables agroup is expected to include.

Mass Spectrometry Computer Program Product

In various embodiments, a computer program product includes anon-transitory and tangible computer-readable storage medium whosecontents include a program with instructions being executed on aprocessor so as to perform a method for grouping fragment ions of amolecule using tandem mass spectrometry with ramps in collision energy.This method is performed by a system that includes one or more distinctsoftware modules.

FIG. 20 is a schematic diagram of a system 2000 that includes one ormore distinct software modules that performs a method for groupingfragment ions of a molecule using mass spectrometry with ramps incollision energy, in accordance with various embodiments. System 2000includes measurement module 2010 and grouping module 2020.

Measurement module 2010 obtains a plurality of variables for a pluralityof fragment ions produced by a mass spectrometer. The mass spectrometerfragments and analyzes a known molecule using a plurality of differentcollision energies.

Grouping module 2020 performs principal component analysis on theplurality of variables. Grouping module 2020 selects a number ofprincipal components produced by the principal component analysis.Grouping module 2020 creates a subset principal component space havingthe number of principal components. Grouping module 2020 selects avariable in the subset principal component space. Grouping module 2020defines a spatial angle around a vector extending from an origin to thevariable. Grouping module 2020 selects a set of one or more variableswithin the spatial angle of the vector. Grouping module 2020 assigns theset to a group, if the set comprises a minimum number of variables. Thegroup identifies correlated variables. The minimum number of variablesis the number of correlated variables a group is expected to include.

While the present teachings are described in conjunction with variousembodiments, it is not intended that the present teachings be limited tosuch embodiments. On the contrary, the present teachings encompassvarious alternatives, modifications, and equivalents, as will beappreciated by those of skill in the art.

Further, in describing various embodiments, the specification may havepresented a method and/or process as a particular sequence of steps.However, to the extent that the method or process does not rely on theparticular order of steps set forth herein, the method or process shouldnot be limited to the particular sequence of steps described. As one ofordinary skill in the art would appreciate, other sequences of steps maybe possible. Therefore, the particular order of the steps set forth inthe specification should not be construed as limitations on the claims.In addition, the claims directed to the method and/or process should notbe limited to the performance of their steps in the order written, andone skilled in the art can readily appreciate that the sequences may bevaried and still remain within the spirit and scope of the variousembodiments.

What is claimed is:
 1. A system for grouping fragment ions of a moleculeusing mass spectrometry with ramps in collision energy, comprising: amass spectrometer that fragments and analyzes a known molecule using aplurality of different collision energies producing a plurality ofvariables for a plurality of fragment ions; and a processor thatperforms principal component analysis on the plurality of variables,selects a number of principal components produced by the principalcomponent analysis, creates a subset principal component space havingthe number of principal components, (a) selects a variable in the subsetprincipal component space, (b) defines a spatial angle around a vectorextending from an origin to the variable, (c) selects a set of one ormore variables within the spatial angle of the vector, and (d) assignsthe set to a group, if the set comprises a minimum number of variables,wherein the group identifies correlated variables and wherein theminimum number of variables is a number of correlated variables a groupis expected to include.
 2. The system of claim 1, wherein the processorfurther (e) removes the set of one or more variables from furtheranalysis and repeats steps (a)-(e) until remaining variables notassigned to a group do not exceed a threshold, producing a plurality ofgroups.
 3. The system of claim 2, wherein the processor further ordersthe plurality of groups by collision energy.
 4. The system of claim 2,wherein the processor further maps one or more groups of the pluralityof groups to a fragmentation pathway.
 5. The system of claim 2, whereinthe processor creates a profile plot that includes a fragment ion fromeach group of the plurality of groups.
 6. The system of claim 5, whereinthe processor compares peak shapes of the profile plot to map a fragmention to a fragmentation pathway.
 7. The system of claim 5, wherein theprocessor compares peak intensities of the profile plot to map afragment ion to a fragmentation pathway.
 8. A method for groupingfragment ions of a molecule using mass spectrometry with ramps incollision energy, comprising: obtaining a plurality of variables for aplurality of fragment ions produced by a mass spectrometer thatfragments and analyzes a known molecule using a plurality of differentcollision energies; performing principal component analysis on theplurality of variables; selecting a number of principal componentsproduced by the principal component analysis; creating a subsetprincipal component space having the number of principal components; (a)selecting a variable in the subset principal component space; (b)defining a spatial angle around a vector extending from an origin to thevariable; (c) selecting a set of one or more variables within thespatial angle of the vector; and (d) assigning the set to a group, ifthe set comprises a minimum number of variables, wherein the groupidentifies correlated variables and wherein the minimum number ofvariables is a number of correlated variables a group is expected toinclude.
 9. The method of claim 8, further comprising (e) removing theset of one or more variables from further analysis and repeating steps(a)-(e) until remaining variables not assigned to a group do not exceeda threshold, producing a plurality of groups.
 10. The method of claim 9,further comprising ordering the plurality of groups by collision energy.11. The method of claim 9, further comprising mapping one or more groupsof the plurality of groups to a fragmentation pathway.
 12. The method ofclaim 9, further comprising creating a profile plot that includes afragment ion from each group of the plurality of groups.
 13. The methodof claim 12, further comprising comparing peak shapes of the profileplot to map a fragment ion to a fragmentation pathway.
 14. The method ofclaim 12, further comprising comparing peak intensities of the profileplot to map a fragment ion to a fragmentation pathway.
 15. A computerprogram product, comprising a non-transitory and tangiblecomputer-readable storage medium whose contents include a program withinstructions being executed on a processor so as to perform a method forgrouping fragment ions of a molecule using mass spectrometry with rampsin collision energy, the method comprising: providing a system, whereinthe system comprises one or more distinct software modules, and whereinthe distinct software modules comprise a measurement module and agrouping module; obtaining a plurality of variables for a plurality offragment ions produced by a mass spectrometer that fragments andanalyzes a known molecule using a plurality of different collisionenergies using the measurement module; performing principal componentanalysis on the plurality of variables using the grouping module;selecting a number of principal components produced by the principalcomponent analysis using the grouping module; creating a subsetprincipal component space having the number of principal componentsusing the grouping module; (a) selecting a variable in the subsetprincipal component space using the grouping module; (b) defining aspatial angle around a vector extending from an origin to the variableusing the grouping module; (c) selecting a set of one or more variableswithin the spatial angle of the vector using the grouping module; and(d) assigning the set to a group, if the set comprises a minimum numberof variables using the grouping module, wherein the group identifiescorrelated variables and wherein the minimum number of variables is anumber of correlated variables a group is expected to include.
 16. Thecomputer program product of claim 15, further comprising (e) removingthe set of one or more variables from further analysis and repeatingsteps (a)-(e) until remaining variables not assigned to a group do notexceed a threshold using the grouping module, producing a plurality ofgroups.
 17. The computer program product of claim 16, further comprisingordering the plurality of groups by collision energy.
 18. The computerprogram product of claim 16, further comprising mapping one or moregroups of the plurality of groups to a fragmentation pathway.
 19. Thecomputer program product of claim 16, further comprising creating aprofile plot that includes a fragment ion from each group of theplurality of groups.
 20. The computer program product of claim 19,further comprising comparing peak shapes or intensities of the profileplot to map a fragment ion to a fragmentation pathway.